CN112731292B - Low-altitude flight target signal time delay estimation method with local IMF energy weighting - Google Patents
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Abstract
The invention relates to a low-altitude flight target signal time delay estimation method with local IMF energy weighting, which comprises the steps of firstly, carrying out Empirical Mode Decomposition (EMD) on signals, selecting effective eigenmode functions (IMFs) according to a spectrum correlation criterion, then selecting effective IMFs with consistent spectrums among the signals to form a local IMF, carrying out cross-correlation time delay estimation on the local IMFs to obtain a time delay estimation value, and finally, obtaining a time delay estimation result among the signals according to an energy ratio weighting estimation criterion. The method improves the accuracy and the robustness of the time delay estimation result between signals under the interference of the correlation noise, and is particularly suitable for time delay estimation of acoustic signals and seismic signals generated by a moving target source, such as low-altitude flight targets of jet planes, helicopters and the like. The method provided by the invention is suitable for the field of acoustic sensor array or geophone array signal processing, and can solve the problems of positioning and tracking of low-altitude flight targets.
Description
Technical field:
the invention relates to a time delay estimation method of a moving target signal, in particular to a time delay estimation method of a low-altitude flight target signal by weighting local IMF energy by an acoustic sensor array or a geophone array signal.
The background technology is as follows:
the time delay estimation is a basic parameter for representing signals, is one of basic tools for statistical signal processing, and is widely applied to the fields of sound source localization, radar, seismic exploration, array signal processing and the like. The time delay estimation is to estimate the time difference of signals received by different sensors in the array, so as to determine the information of the azimuth, the speed, the distance and the like of the target. The traditional time delay estimation method comprises the following steps: correlation methods, generalized cross-correlation methods, frequency domain cross-power spectroscopy, phase spectroscopy, adaptive delay estimation methods and the like, and the methods are mainly used for delay estimation of stationary target signals. The correlation method and the generalized cross-correlation method can accurately estimate time delay when the signal to noise ratio is high, for example Tang Haoyang, as mentioned in GCC time delay estimation algorithm analysis based on a microphone array, the generalized cross-correlation method has poor estimation precision and instability when the signal to noise ratio is low, and the generalized cross-correlation method cannot determine a weighting function when the signal to noise ratio is low, and the time delay estimation error is increased by selecting an estimation value instead. For the frequency domain cross-power spectrometry, liang Yu-the time delay estimation based on the generalized cross-correlation algorithm is greatly reduced if noise of correlation exists between signals. The phase spectrum time delay estimation method needs to set upper and lower limit frequencies for straight line fitting, is effective only for broadband signals, has high time delay estimation precision when the signal to noise ratio is large, does not need interpolation, and can greatly reduce the time delay estimation precision when the signal to noise ratio is low or frequency points with low signal to noise ratio exist in the signals. The adaptive time delay estimation method needs to set parameters and iteration initial values, calculates time delay through adaptive learning, increases calculated amount, and has poor application effect in practical environment on the premise that background noise is uncorrelated Gaussian noise. Specific information related to the phase spectrum method and the adaptive delay estimation method can be researched by referring to Qiao Zhenyue-minimum mean square error adaptive delay estimation algorithm. It can be seen that the above methods are all used for estimating signal delay through a longer time window, are mainly applied to stationary target signal processing, and are poor in effect when the signal is interfered by noise, especially correlation noise. When we perform delay estimation on the signal of the moving object, the above methods are poor in effect for the fast moving source signal requiring a short time window for delay estimation, and are susceptible to interference of correlated noise. Therefore, in the environment of being interfered by correlated noise (correlated Gaussian interference, correlated random noise, etc.), when the time delay estimation is performed on the moving target signal in a short time window, a time delay estimation method with good anti-noise effect, more accuracy and more robustness is needed.
The invention comprises the following steps:
the invention aims to provide a low-altitude flight target signal time delay estimation method with local IMF energy weighting aiming at the defects of the prior art.
The main idea of the invention is that: when a mobile sound source positioning experiment is carried out in the field, the statistical characteristics of sound signals generated by a moving target are not obvious, and particularly for a low-altitude flying target moving at a high speed, the smaller the delay estimation window is, the better the delay estimation window is, the worse the estimation precision is when the delay estimation window is smaller, the noise interference is easy to be caused, and the requirement of the delay estimation of the moving target signal cannot be well met. Based on the above, the invention provides a low-altitude flight target signal time delay estimation method with local IMF energy weighting, which comprises the steps of firstly carrying out EMD decomposition on signals, selecting effective IMFs according to a spectrum correlation criterion, further selecting the effective IMFs with consistent spectrums among the signals on the basis to form the local IMFs, carrying out cross-correlation time delay estimation on the local IMFs to obtain time delay estimation values thereof, and finally obtaining the time delay estimation values among the signals according to an energy ratio weighting estimation criterion. The method not only can suppress the related noise interference, but also greatly improves the accuracy and the robustness of the time delay estimation result of the moving target signal in a short time window by constructing a narrow-band local IMF. The method is particularly suitable for acoustic signals and seismic signals generated by a moving target source, and can exert the advantages under the condition of correlated noise interference.
The invention aims at realizing the following technical scheme:
the low-altitude flight target signal time delay estimation method with the local IMF energy weighting comprises the following steps:
a. collecting low altitude received by two paths of sensorsA flight target signal, a sampling sequence s of two paths of target signals with the same time point and the length of L is intercepted 1 (t) and s 2 (t) if the signal propagation speed is V and the distance between the two paths of sensors is C, the selection criterion of the length L is l=3c/V;
b. for signal sequence s 1 (t) performing Empirical Mode Decomposition (EMD) to obtain Z eigenmode function (IMF) components (I) 1 ,I 2 ,…,I z ) A margin R 1 For s 1 Fourier transforming the (t) and Z IMF components to obtain corresponding spectral curves F (S) 1 )、(F(I 1 ),F(I 2 ),…,F(I Z ))；
c. According to the following sequence (F (I) 1 ),F(I 2 ),…,F(I Z ) F (S) 1 ) Is a correlation coefficient of (a):
obtaining a correlation coefficient sequence C= [ C ] 1 ,C 2 ,…,C Z ]Finding out the first correlation coefficient mutation peak value point O in the correlation coefficient sequence, wherein the eigenvalue function component corresponding to the point O is I O The correlation coefficient is C O From sequence [ C O ,C O+1 ,…,C Z ]Finding a point P with a first correlation coefficient smaller than B, wherein the intrinsic mode function component corresponding to the point P is I P The correlation coefficient is C P The value B is 0.1, and the IMF component (I O ,I O+1 ,…,I P-1 ) Considered as the effective IMF component;
d. for a sequence of sampled signals s 2 (t) repeating steps b to c in sequence to obtain s 2 Effective IMF component (J) M ,J M+1 ,…,J N )；
e. Comparison (I) O ,I O+1 ,…,I P-1 ) And (J) M ,J M+1 ,…,J N ) Based on the number of IMF components, if it is (I O ,I O+1 ,…,I P-1 ) The number of IMF components is small, and (I O ,I O+1 ,…,I P-1 ) Each IMF component of (J) M ,J M+1 ,…,J N ) Spectral similarity between each IMF component of (I) O Corresponding to a group of spectrum similarity results, IMF component I O+1 And correspondingly, another set of spectrum similarity results, and so on. The spectral similarity between the effective IMF components of the two signals is calculated by defining the following equation:
wherein x is s 1 A certain effective IMF component of (t), y being s 2 A certain effective IMF component of (t), function F representing a fourier transform;
f. selecting the maximum value of each group of spectrum similarity results to form a spectrum similarity result sequence S O ,S O+1 ,…,S P-1 ]Normalizing the sequence element values and making a maximum value relation curve of the spectrum similarity result, and selecting that the maximum value of the spectrum similarity result on the curve is larger than B 1 The spectral similarity of the two IMF components corresponding to the values is highest, these IMF components are named local IMF components, (I) O ,I O+1 ,…,I P-1 ) Is (I) 11 ,I 12 ,…,I 1W )，(J M ,J M+1 ,…,J N ) Is (J) 11 ,J 12 ,…,J 1W )，B 1 Taking a value of 0.7;
g. and f, respectively carrying out cross-correlation time delay estimation on each two local IMF components selected in the step f to obtain a time delay estimated value sequence X= [ X ] 1 ,X 2 ,…,X W ]According to the following formula (I 11 ,I 12 ,…,I 1W ) The energy ratio of each component:
wherein the function E is a signal energy function;
h、s 1 (t) and s 2 The final delay estimate of (t) is:
the beneficial effects are that: through verification, the local IMF energy weighted low-altitude flight target signal time delay estimation method provided by the invention greatly improves the accuracy of the motion target signal time delay estimation result, is more accurate and stable under the condition of correlated noise interference, is not only suitable for stationary target source signal time delay estimation, but also has more advantages in motion target source signal time delay estimation compared with other time delay estimation methods, and can obtain an accurate result when the time delay window length is three times of sound path difference time, and the result error of the traditional generalized cross correlation method is larger. The method widens the application range of time delay estimation, has less requirement on signal priori knowledge, greatly improves the robustness of time delay estimation results, and has better application effect in the fields of acoustic sensor array, geophone array target positioning and the like.
Description of the drawings:
FIG. 1 illustrates a target simulation signal of a low-altitude flight target signal delay estimation method with local IMF energy weighting
Fig. 2 local IMF cross-correlation delay estimation results
Fig. 3 generalized cross-correlation delay estimation results
The specific embodiment is as follows:
the following is a further detailed description with reference to the accompanying drawings:
a. the low-altitude flight target signals received by the two paths of sensors are collected, and in the example, simulation signals are used for replacing the low-altitude flight target signals, wherein the simulation signals consist of single-frequency signals, variable-frequency signals and random noise. The sampling rate of the simulation signal is 1000, the time length is 10s, and as shown in fig. 1, the simulation signal is delayed for 0.1s to obtain another path of target signal. Respectively intercepting two paths of target signals with the same time point and the length L of 0.3s to obtain a sampling sequence s 1 (t) and s 2 (t)；
b. For signal sequence s 1 (t) EMD decompositionObtaining Z IMF components (I 1 ,I 2 ,…,I z ) A margin R 1 For s 1 Fourier transforming the (t) and Z IMF components to obtain corresponding spectral curves F (S) 1 )、(F(I 1 ),F(I 2 ),…,F(I Z ))；
c. According to the following sequence (F (I) 1 ),F(I 2 ),…,F(I Z ) F (S) 1 ) Is a correlation coefficient of (a):
obtaining a correlation coefficient sequence C= [ C ] 1 ,C 2 ,…,C Z ]Finding out the first correlation coefficient mutation peak value point O in the correlation coefficient sequence, wherein the eigenvalue function component corresponding to the point O is I O The correlation coefficient is C O From sequence [ C O ,C O+1 ,…,C Z ]Finding a point P with a first correlation coefficient smaller than B, wherein the intrinsic mode function component corresponding to the point P is I P The correlation coefficient is C P The value B is 0.1, and the IMF component (I O ,I O+1 ,…,I P-1 ) Considered as the effective IMF component;
d. for a sequence of sampled signals s 2 (t) repeating steps b to c in sequence to obtain s 2 Effective IMF component (J) M ,J M+1 ,…,J N )；
e. Comparison (I) O ,I O+1 ,…,I P-1 ) And (J) M ,J M+1 ,…,J N ) Based on the number of IMF components, if it is (I O ,I O+1 ,…,I P-1 ) The number of IMF components is small, and (I O ,I O+1 ,…,I P-1 ) Each IMF component of (J) M ,J M+1 ,…,J N ) Spectral similarity between each IMF component of (I) O Corresponding to a group of spectrum similarity results, IMF component I O+1 And correspondingly, another set of spectrum similarity results, and so on. Defining the following calculates the spectral similarity between the effective IMF components of the two signalsSex:
wherein x is s 1 A certain effective IMF component of (t), y being s 2 A certain effective IMF component of (t), function F representing a fourier transform;
f. selecting the maximum value of each group of spectrum similarity results to form a spectrum similarity result sequence S O ,S O+1 ,…,S P-1 ]Normalizing the sequence element values and making a maximum value relation curve of the spectrum similarity result, and selecting that the maximum value of the spectrum similarity result on the curve is larger than B 1 The spectral similarity of the two IMF components corresponding to the values is highest, these IMF components are named local IMF components, (I) O ,I O+1 ,…,I P-1 ) Is (I) 11 ,I 12 ,…,I 1W )，(J M ,J M+1 ,…,J N ) Is (J) 11 ,J 12 ,…,J 1W )，B 1 Taking a value of 0.7;
g. and f, respectively carrying out cross-correlation time delay estimation on each two local IMF components selected in the step f to obtain a time delay estimated value sequence X= [ X ] 1 ,X 2 ,…,X W ]Fig. 2 shows a delay estimation result obtained by cross-correlating two local IMFs, which is 100ms, that is, 0.1s, and matches the simulation data result. In contrast, fig. 3 shows the delay estimation result obtained by the generalized cross-correlation method, which has more peaks (37 ms and 100 ms), no prominence and poor accuracy. Then, according to the following formula (I 11 ,I 12 ,…,I 1W ) The energy ratio of each component:
wherein the function E is a signal energy function;
h、s 1 (t) and s 2 The final delay estimate of (t) is:
Claims (1)
1. the low-altitude flight target signal time delay estimation method for the local IMF energy weighting is characterized by comprising the following steps of:
a. collecting low-altitude flight target signals received by two paths of sensors, and intercepting sampling sequences s of two paths of target signals with the same time point and the length of L 1 (t) and s 2 (t) if the signal propagation speed is V and the distance between the two paths of sensors is C, the selection criterion of the length L is l=3c/V;
b. for signal sequence s 1 (t) performing Empirical Mode Decomposition (EMD) to obtain Z eigenmode function (IMF) components (I) 1 ,I 2 ,…,I z ) A margin R 1 For s 1 Fourier transforming the (t) and Z IMF components to obtain corresponding spectral curves F (S) 1 )、(F(I 1 ),F(I 2 ),…,F(I Z ))；
c. According to the following sequence (F (I) 1 ),F(I 2 ),…,F(I Z ) F (S) 1 ) Is a correlation coefficient of (a):
obtaining a correlation coefficient sequence C= [ C ] 1 ,C 2 ,…,C Z ]Finding out the first correlation coefficient mutation peak value point O in the correlation coefficient sequence, wherein the eigenvalue function component corresponding to the point O is I O The correlation coefficient is C O From sequence [ C O ,C O+1 ,…,C Z ]Finding a point P with a first correlation coefficient smaller than B, wherein the intrinsic mode function component corresponding to the point P is I P The correlation coefficient is C P The value B is 0.1, and the IMF component (I O ,I O+1 ,…,I P-1 ) Considered as the effective IMF component;
d. for a sequence of sampled signals s 2 (t) repeating steps b to c in sequence to obtain s 2 Effective IMF component (J) M ,J M+1 ,…,J N )；
e. Comparison (I) O ,I O+1 ,…,I P-1 ) And (J) M ,J M+1 ,…,J N ) Based on the number of IMF components, if it is (I O ,I O+1 ,…,I P-1 ) The number of IMF components is small, and (I O ,I O+1 ,…,I P-1 ) Each IMF component of (J) M ,J M+1 ,…,J N ) Spectral similarity between each IMF component of (I) O Corresponding to a group of spectrum similarity results, IMF component I O+1 Corresponding to another set of spectrum similarity results, and so on; the spectral similarity between the effective IMF components of the two signals is calculated by defining the following equation:
wherein x is s 1 A certain effective IMF component of (t), y being s 2 A certain effective IMF component of (t), function F representing a fourier transform;
f. selecting the maximum value of each group of spectrum similarity results to form a spectrum similarity result sequence S O ,S O+1 ,…,S P-1 ]Normalizing the sequence element values and making a maximum value relation curve of the spectrum similarity result, and selecting that the maximum value of the spectrum similarity result on the curve is larger than B 1 The spectral similarity of the two IMF components corresponding to the values is highest, these IMF components are named local IMF components, (I) O ,I O+1 ,…,I P-1 ) Is (I) 11 ,I 12 ,…,I 1W )，(J M ,J M+1 ,…,J N ) Is (J) 11 ,J 12 ,…,J 1W )，B 1 Taking a value of 0.7;
g. and f, respectively carrying out cross-correlation time delay estimation on each two local IMF components selected in the step f to obtain a time delay estimated value sequence X= [ X ] 1 ,X 2 ,…,X W ]According to the following formula (I 11 ,I 12 ,…,I 1W ) The energy ratio of each component:
wherein the function E is a signal energy function;
h、s 1 (t) and s 2 The final delay estimate of (t) is:
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